During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps, which in the real-world limit the practicality of these algorithms ...

The thesis is a compilation of two independent works. In the first work, we develop novel weight assignment procedure, which helps us develop several schedule based algorithms. Learning the value function of a given policy ...

The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average ...

Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based ...

In this thesis we will look at various Reinforcement Learning algorithms. We will look at algorithms for various cost criteria or reward objectives namely Finite Horizon, Discounted Cost, Risk-Sensitive Cost. For Finite ...

Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents ...

In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ...

Intelligent Electronic Devices (IEDs) are essential components of modern power grids, functioning as microprocessor-based controllers that facilitate communication, monitoring, protection, and control within Supervisory ...

A fundamental question in a sequential decision making setting under uncertainty is “how to allocate resources amongst competing entities so as to maximize the rewards accumulated in the long run?”. The resources allocated ...

Many sequential decision making problems under uncertainty arising in engineering, science and economics are often modelled as Markov Decision Processes (MDPs). In the setting of MDPs, the goal is to and a state dependent ...